Collaborative Filtering Is Not Enough? Experiments with a Mixed-Model Recommender for Leisure Activities

Authors:
Nicolas Ducheneaut;Kurt Partridge;Qingfeng Huang;Bob Price;Mike Roberts;Ed H. Chi;Victoria Bellotti;Bo Begole
Affiliations:
Palo Alto Research Center, Palo Alto, USA CA 94304;Palo Alto Research Center, Palo Alto, USA CA 94304;Palo Alto Research Center, Palo Alto, USA CA 94304;Palo Alto Research Center, Palo Alto, USA CA 94304;Palo Alto Research Center, Palo Alto, USA CA 94304;Palo Alto Research Center, Palo Alto, USA CA 94304;Palo Alto Research Center, Palo Alto, USA CA 94304;Palo Alto Research Center, Palo Alto, USA CA 94304
Venue:
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Year:
2009

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Abstract

Collaborative filtering (CF) is at the heart of most successful recommender systems nowadays. While this technique often provides useful recommendations, conventional systems also ignore data that could potentially be used to refine and adjust recommendations based on a user's context and preferences. The problem is particularly acute with mobile systems where information delivery often needs to be contextualized. Past research has also shown that combining CF with other techniques often improves the quality of recommendations. In this paper, we present results from an experiment assessing user satisfaction with recommendations for leisure activities that are obtained from different combinations of these techniques. We show that the most effective mix is highly dependent on a user's familiarity with a geographical area and discuss the implications of our findings for future research.